



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							StableDiffusionInpaintPipeline
		_lowerCamelCase      	=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCamelCase      	=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCamelCase      	=							frozenset(
		    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		_lowerCamelCase      	=							frozenset([]		)





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								torch.manual_seed(0 )
								snake_case_ :			Union[str, Any]            =      UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=9				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=3_2				,     attention_head_dim=(2, 4)				,     use_linear_projection=_lowercase				,     )
								snake_case_ :			Dict            =      PNDMScheduler(skip_prk_steps=_lowercase )
								torch.manual_seed(0 )
								snake_case_ :			str            =      AutoencoderKL(
								    block_out_channels=[3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,     latent_channels=4				,     sample_size=1_2_8				,     )
								torch.manual_seed(0 )
								snake_case_ :			Union[str, Any]            =      CLIPTextConfig(
								    bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     hidden_act="""gelu"""				,     projection_dim=5_1_2				,     )
								snake_case_ :			Dict            =      CLIPTextModel(_lowercase )
								snake_case_ :			Optional[Any]            =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

								snake_case_ :			int            =      {
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=0 )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(_lowercase ) ).to(_lowercase )
								snake_case_ :			str            =      image.cpu().permute(0				,     2				,     3				,     1 )[0]
								snake_case_ :			Tuple            =      Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((6_4, 6_4) )
								snake_case_ :			Any            =      Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((6_4, 6_4) )
								if str(_lowercase ).startswith("""mps""" ):
														snake_case_ :			str            =      torch.manual_seed(_lowercase )
								else:
														snake_case_ :			List[str]            =      torch.Generator(device=_lowercase ).manual_seed(_lowercase )
								snake_case_ :			Optional[Any]            =      {
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": init_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
								snake_case_ :			List[str]            =      self.get_dummy_components()
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline(**_lowercase )
								snake_case_ :			Dict            =      sd_pipe.to(_lowercase )
								sd_pipe.set_progress_bar_config(disable=_lowercase )

								snake_case_ :			Optional[int]            =      self.get_dummy_inputs(_lowercase )
								snake_case_ :			List[str]            =      sd_pipe(**_lowercase ).images
								snake_case_ :			Tuple            =      image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								snake_case_ :			Optional[int]            =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




@slow
@require_torch_gpu
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			List[Any]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
								snake_case_ :			Dict            =      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy""" )

								snake_case_ :			str            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(_lowercase				,     safety_checker=_lowercase )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing()

								snake_case_ :			List[Any]            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			List[str]            =      torch.manual_seed(0 )
								snake_case_ :			Dict            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     output_type="""np"""				,     )
								snake_case_ :			Tuple            =      output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image ).max() < 9E-3





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			str            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			Tuple            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
								snake_case_ :			Union[str, Any]            =      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )

								snake_case_ :			str            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(
								    _lowercase				,     torch_dtype=torch.floataa				,     safety_checker=_lowercase				,     )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing()

								snake_case_ :			Union[str, Any]            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			Optional[Any]            =      torch.manual_seed(0 )
								snake_case_ :			Any            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     output_type="""np"""				,     )
								snake_case_ :			str            =      output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image ).max() < 5E-1





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								snake_case_ :			int            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			Optional[int]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )

								snake_case_ :			int            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			List[str]            =      PNDMScheduler.from_pretrained(_lowercase				,     subfolder="""scheduler""" )
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(
								    _lowercase				,     safety_checker=_lowercase				,     scheduler=_lowercase				,     torch_dtype=torch.floataa				,     )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing(1 )
								pipe.enable_sequential_cpu_offload()

								snake_case_ :			Any            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			List[Any]            =      torch.manual_seed(0 )
								snake_case_ :			Any            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     num_inference_steps=2				,     output_type="""np"""				,     )

								snake_case_ :			Dict            =      torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 1_0**9


def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 if n == 0:
  return 0
 UpperCamelCase			  =      float("""-inf"""       )
 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max(
      UpperCamelCase_							,					prices[i - 1] + naive_cut_rod_recursive(n - i							,					UpperCamelCase_       )       )

 return max_revue





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 return _top_down_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  UpperCamelCase			  =      float("""-inf"""       )
  for i in range(1							,					n + 1       ):
   UpperCamelCase			  =      max(
       UpperCamelCase_							,					prices[i - 1] + _top_down_cut_rod_recursive(n - i							,					UpperCamelCase_							,					UpperCamelCase_       )							,					)

  UpperCamelCase			  =      max_revenue

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 UpperCamelCase			  =      0

 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max_rev[i]
  for j in range(1							,					i + 1       ):
   UpperCamelCase			  =      max(UpperCamelCase_							,					prices[j - 1] + max_rev[i - j]       )

  UpperCamelCase			  =      max_revenue_i

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 if n < 0:
  UpperCamelCase			  =      f"""n must be greater than or equal to 0. Got n = {n}"""
  raise ValueError(UpperCamelCase_       )

 if n > len(UpperCamelCase_       ):
  UpperCamelCase			  =      (
      """Each integral piece of rod must have a corresponding price. """
      f"""Got n = {n} but length of prices = {len(UpperCamelCase_       )}"""
  )
  raise ValueError(UpperCamelCase_       )





def 			lowercase(	)		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      [6, 10, 12, 15, 20, 23]
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 UpperCamelCase			  =      36

 UpperCamelCase			  =      top_down_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      bottom_up_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      naive_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_       )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
     main()
